Published in The Fifteenth International Symposium on Combinatorial Search 2022, 2022
This paper revisits beam search from the perspective of memory-bounded search and proposes a strategy for improving it.
Recommended citation: Chao Gao et al., A Memory-Bounded Best-First Beam Search and Its Application to Scheduling Halide Programs http://search-conference.org/`
Published in AAAI, 2022
This paper studies sample average approximation for stochastic optimization.
Recommended citation: Wang et al., Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability https://arxiv.org/abs/2112.05368
Published in ICTAI, 2021
This describes our work of improving TVM auto-scheduler using Bandit-based RL.
Recommended citation: Gao et al., Bansor: Improving Tensor Program Auto-Scheduling with Bandit Based Reinforcement Learning #
Published in Springer, 2020
This describes the pommmerman competition at neurips 2018.
Recommended citation: Resnick, C., Gao, C., Marton, G., Osogami, T., Pang, L. and Takahashi, T., 2020. Pommerman & neurips 2018. In The NeurIPS-18 Competition (pp. 11-36). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-29135-8_2
Published in AAAI Conference on AI and Interactive Digital Entertainment, 2019
Recommended citation: Gao, C., Kartal, B., Hernandez-Leal, P. and Taylor, M.E., 2019, October. On hard exploration for reinforcement learning: A case study in pommerman. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 15, No. 1, pp. 24-30). https://ojs.aaai.org/index.php/AIIDE/article/view/5220/5076
Published in CVPR 2019, 2019
Recommended citation: Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M. and Jagersand, M., 2019. Basnet: Boundary-aware salient object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7479-7489). https://openaccess.thecvf.com/content_CVPR_2019/papers/Qin_BASNet_Boundary-Aware_Salient_Object_Detection_CVPR_2019_paper.pdf
Published in IJCAI 2018, 2018
Recommended citation: Gao, C., Müller, M. and Hayward, R., 2018, January. Three-Head Neural Network Architecture for Monte Carlo Tree Search. In IJCAI (pp. 3762-3768). https://www.ijcai.org/proceedings/2018/0523.pdf
Published in IEEE Transaction on Games, 2017
Recommended citation: Gao, C., Hayward, R. and Müller, M., 2017. Move prediction using deep convolutional neural networks in Hex. IEEE Transactions on Games, 10(4), pp.336-343. https://ieeexplore.ieee.org/abstract/document/8226781
Published in IJCAI 2017, 2017
Recommended citation: Gao, C., Müller, M. and Hayward, R., 2017, January. Focused Depth-first Proof Number Search using Convolutional Neural Networks for the Game of Hex. In IJCAI (pp. 3668-3674). https://www.ijcai.org/proceedings/2017/513
Published in European Journal of Operational Research, 2017
Recommended citation: Gao, C., Lu, G., Yao, X. and Li, J., 2017. An iterative pseudo-gap enumeration approach for the Multidimensional Multiple-choice Knapsack Problem. European Journal of Operational Research, 260(1), pp.1-11. https://www.sciencedirect.com/science/article/abs/pii/S0377221716309675
Published in European Journal of Operational Research, 2015
Recommended citation: Gao, C., Yao, X., Weise, T. and Li, J., 2015. An efficient local search heuristic with row weighting for the unicost set covering problem. European Journal of Operational Research, 246(3), pp.750-761. https://www.sciencedirect.com/science/article/abs/pii/S0377221715004282